The effects of virutal reality on procedural pain and anxiety in pediatrics: A systematic review and meta-analysis

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The Effects of Virtual Reality on Procedural Pain and Anxiety in

Pediatrics: A Systematic Review and Meta-Analysis

Rikke Nordgård¹and Torstein Låg^1,2*

1Department of Psychology, Faculty of Health Sciences, University of Tromsø–The Arctic University of Norway, Tromsø, Norway,

2University Library, University of Tromsø–The Arctic University of Norway, Tromsø, Norway

Distraction and procedural preparation techniques are frequently used to manage pain and anxiety in children undergoing medical procedures. An increasing number of studies have indicated that Virtual Reality (VR) can be used to deliver these interventions, but treatment effects vary greatly. The present study is a systematic review and meta-analysis of studies that have used VR to reduce procedural pain and anxiety in children. It is thefirst meta- analytic assessment of the potential influence of technical specifications (immersion) and degree of user-system interactivity on treatment effects. 65 studies were identified, of which 42 reported pain outcomes and 35 reported anxiety outcomes. Results indicate large effect sizes in favor of VR for both outcomes. Larger effects were observed in dental studies and studies that used non-interactive VR. No relationship was found between the degree of immersion or participant age and treatment effects. Most studies were found to have a high risk of bias and there are strong indications of publication bias. The results and their implications are discussed in context of these limitations, and modified effect sizes are suggested. Finally, recommendations for future investigations are provided.

Keywords: virtual reality, pain, anxiety, pediatrics, distraction analgesia, procedural preparation, meta-analysis, systematic review

INTRODUCTION

The management of pain and anxiety in children undergoing medical procedures remains sub- optimal (Stevens et al., 2011;Birnie et al., 2014;Friedrichsdorf and Goubert, 2020). As well as causing excessive and unnecessary suffering, undertreated procedural distress may have long-term negative effects on child health and development, as well as treatment outcomes (Young, 2005). Current best practice guidelines recommend that non-pharmacological interventions are routinely implemented in treatment plans (Wilson-Smith, 2011). Two common, non-pharmacological approaches are distraction and procedural preparation. Distraction involves the use of distractors like music and television to divert attention away from noxious stimuli, whereas preparation techniques usually entail information about the procedure or exposure to the procedural setting (e.g., a tour of the clinic). Over the last couple of decades, researchers have explored whether virtual reality (VR) can be used to deliver and possibly enhance distraction and preparation interventions in pediatrics.

Previous reviews have indicated the potential of VR in pediatrics (e.g.,Indovina et al., 2018;Eijlers et al., 2019a;Georgescu et al., 2020;Lambert et al., 2020). Its immersive, interactive nature is thought to provide particularly captivating distraction, as well as a cost-effective and engaging medium for

Edited by:

Marientina Gotsis, University of Southern California, United States

Reviewed by:

Cho Lee Wong, The Chinese University of Hong Kong, China Vangelis Lympouridis, University of Southern California, United States

*Correspondence:

Torstein Låg torstein.lag@uit.no

Specialty section:

This article was submitted to Virtual Reality in Medicine, a section of the journal Frontiers in Virtual Reality

Received:23 April 2021 Accepted:24 June 2021 Published:15 July 2021 Citation:

Nordgård R and Låg T (2021) The Effects of Virtual Reality on Procedural Pain and Anxiety in Pediatrics: A Systematic Review and Meta-Analysis.

Front. Virtual Real. 2:699383.

doi: 10.3389/frvir.2021.699383

Frontiers in Virtual Reality | www.frontiersin.org 1 July 2021 | Volume 2 | Article 699383

SYSTEMATIC REVIEW published: 15 July 2021 doi: 10.3389/frvir.2021.699383

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procedural preparation. However, previous meta-analyses have revealed great heterogeneity in treatment effects and little is known about the underlying mechanisms and factors that determine the effectiveness of VR interventions (Li et al., 2011).

The present study is a systematic review and meta-analysis of studies that have used VR to reduce procedural pain and anxiety in pediatrics. To address the variability of effect sizes that have been observed across studies, the potential inﬂuence of various VR, procedural, and participant characteristics will be explored.

The main focus will be on characteristics of VR systems, including the technical speciﬁcations and degree of user- system interaction. While some evidence suggest that VR characteristics inﬂuence treatment effects (e.g.,Hoffman et al., 2006;Wender et al., 2009;Johnson and Coxon, 2016), this has not yet been assessed in a meta-analysis.

Virtual Reality in Healthcare

Virtual reality (VR) may be described as an interactive, immersive, computer-generated environment or experience (Gigante, 1993; Pan and Hamilton, 2018). Typically presented on a head-mounted display (HMD), the screens are positioned close to the users’ eyes with full or partial occlusion of their physical surroundings. Images are often three-dimensional and continuously adjusted in accordance with the user’s head movements (Slater and Sanchez-Vives, 2016). Such features contribute to the sense of being surrounded by or present in the virtual environment that is unique to VR.

Various applications of VR in health have been explored, including in the assessment and treatment of patients. Reviews of the literature have reported signiﬁcant methodological issues and a need for further research, but nevertheless indicate a considerable potential for VR in various clinical settings. For example, VR interventions have been applied in rehabilitation (Laver et al., 2017), habilitation (Snider et al., 2010), psychiatry (Freeman et al., 2017), geriatrics (Neri et al., 2017), and palliative care (Niki et al., 2019). An increasing number of studies have demonstrated its utility in the management of pain and anxiety caused by medical procedures in adult and pediatric populations (Malloy and Milling, 2010;Chan et al., 2018;Eijlers et al., 2019b;

Georgescu et al., 2020).

Procedural Pain and Anxiety in Pediatrics

Children in developed countries undergo an increasing number of potentially painful and anxiety-inducing medical procedures (Curtis et al., 2012). Depending on their age and development, children may experience these procedures as more aversive than adults due to limitations in their ability to communicate their pain and need for pain management, to understand why the procedure is necessary, and to self-regulate (Cohen et al., 2008;

Slifer, 2013;McMurtry et al., 2015). While conditions like cancer and burn injuries often require repeated or particularly distressing procedures (Gandhi et al., 2010; Twycross et al., 2015), routine procedures like venipuncture and immunizations are also known to induce considerable pain and anxiety in children (Reid et al., 2014). If poorly managed, procedural pain and anxiety could have detrimental effects on child health and development, as well as treatment outcomes

(Mathews, 2011;Wilson-Smith, 2011). For example, painful and frightening medical procedures in childhood have been linked to alterations in pain responses later in life (Pate et al., 1996;Taddio et al., 1997; Kennedy et al., 2008), reduced effects of future pharmacological analgesia (Weisman et al., 1998), and development of needle phobia (McMurtry et al., 2015).

The International Association for the Study of Pain (The International Association for the Study of Pain (IASP), 2011) deﬁnes pain as“an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage”. Procedural pain refers to pain associated with medical (or dental) procedures. Procedural anxiety may be described as a response to such procedures characterized by feelings of dread and apprehensiveness, accompanied by physical symptoms such as sweating and increased heart rate (Lavoie, 2013). The relationship between procedural pain and anxiety is intertwined and complex - for example, they frequently co-occur and exacerbate each other (Cohen et al., 2004;McMurtry et al., 2015;Kao and Schwartz, 2019).

The experience of pain is modulated by multiple biological, psychological, and social processes (Bentley, 2014). Some factors known to modulate pain top-down include attention toward painful stimuli, expectation of pain, anxiety, and previous experiences with pain (Linton and Shaw, 2011;Bentley, 2014).

Knowledge of these and other pain-modulating mechanisms have informed the development of various non-pharmacological pain management approaches, including distraction and procedural preparation (Curtis et al., 2012). Current best practice guidelines recommend a combination of pharmacological and non- pharmacological interventions in the treatment of procedural pain and anxiety (e.g.,The Association of Paediatric Anaesthetists of Great Britain and Ireland, 2012). Over the last couple of decades, researchers have explored whether VR can be used to effectively deliver distraction and preparation interventions in pediatrics.

Distraction and Preparation Techniques

Distraction techniques are commonly used during painful or frightening procedures of shorter durations (DeMore and Cohen, 2005). They involve the use of stimuli such as videos, music, and conversation to divert attention away from noxious stimuli (Schechter et al., 2007). No single theory can fully account for the effects of distraction analgesia (DeMore and Cohen, 2005), but they are often understood in terms of attentional capacities. It is assumed that pain perception requires attention, and that by focusing on distractors, less attentional resources are available for pain perception (McCaul and Malott, 1984;Gupta et al., 2017). However, distraction may also work through other mechanisms. For example, pleasant distractors may have inherent positive effects on mood, arousal, and anxiety, all of which have the capacity to alter pain perception (Johnson, 2005). Attention, mood, arousal, and anxiety can all be understood as processes inhibiting nociceptive signals as described in the gate control and neuromatrix theories of pain (Melzack and Wall, 1965;

Melzack, 1999). Due to its immersive, interactive, and

Nordgård and Låg Meta-Analysis of VR in Pediatrics

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multisensory properties, VR is thought to be particularly captivating and thus provide superior distraction (Slifer, 2013).

Another common way of reducing pain and anxiety is procedural preparation, often in the form of a verbal brieﬁng, written materials, or a tour of the clinic (Curtis et al., 2012). Such techniques are meant to reduce anxiety (and possibly also pain) by promoting a sense of control and adaptive behaviors, as well as desensitizing the child to the medical procedure and the setting in which it takes place (Jaaniste et al., 2007;Edward et al., 2015).

Research on virtual reality exposure therapy (VRET) has established that VR can be used to expose users effectively and ecologically to feared stimuli (Botella et al., 2017; Boeldt et al., 2019). Based on theseﬁndings, researchers have recently begun exploring whether VR can be used for procedural preparation (Eijlers et al., 2019a). In addition to exposure to the medical procedure and the environment in which it takes place, VR preparation may involve modeling, instructions, and rehearsal of the procedure (e.g.,Ryu et al., 2018;Han et al., 2019;Liszio et al., 2020).

The In ﬂ uence of Virtual Reality Characteristics

VR systems offer varying degrees of interaction with the user. Less interactive forms of VR include videos converted to a 360/180^° format for viewing on a VR headset. While the user may effect changes in perception (i.e., looking around the virtual environment in 360/180^° through tracking of head movements), he or she is nevertheless a passive spectator of the virtual environment. On the other hand, VR games or simulations may offer interactivitybeyond head tracking, such as navigation in the virtual environment, social interaction with avatars, or manipulation of virtual objects. In the present study, head tracking will be considered an aspect of immersion, and not interactivity.

A potential impact of VR interactivity on procedural pain and anxiety seems plausible. It is generally assumed that active distraction poses greater attentional demands on patients than passive distraction, thus providing superior analgesia (Slifer, 2013). Some studies have reported this pattern for VR speciﬁcally (e.g., Dahlquist et al., 2007; Wender et al., 2009;

Gutiérrez-Maldonado et al., 2011; Gutiérrez-Martínez et al., 2011). In addition, VR interactivity may augment learning and memory (e.g.,James et al., 2002;Tuena et al., 2019), which could be beneﬁcial when used for procedural preparation.

VR systems also vary in terms of technological sophistication, which may be conceptualized as varying degrees of immersion (Nilsson et al., 2016;Agrawal et al., 2019). According toSlater and Wilbur (1997), a highly immersive system should minimize signals from the physical world (e.g., fully occlude the user’s physical surroundings), stimulate multiple senses (e.g., visual, auditive, and tactile), visually surround the user (e.g., a wideﬁeld of view), provide a vivid representation of the virtual environment (e.g., high screen resolution) and match the actions of the participant with the sensory output of the system (e.g., low latency between head rotation and subsequent change in images displayed). This concept of

immersion provides a useful framework for comparison of VR systems, as it can be operationalized and objectively measured (Slater, 2009;Cummings and Bailenson, 2016).

The degree of immersion may have an impact on the effectiveness of VR interventions. According to Slater (2018), higher levels of immersion facilitate the perceptive illusion that the virtual environment is real, which he referred to as presence.

Presence is commonly thought to increase the effectiveness of various forms of VR interventions (Cummings and Bailenson, 2016). VR studies have indicated a possible relationship between immersion/presence and the effectiveness of VR distraction analgesia (e.g.,Hoffman et al., 2004;Hoffman et al., 2006).

Some previous reviews have employed somewhat vague definitions of VR in their inclusion criteria. For example, some authors have specified that they would only include‘immersive VR’ (Chan et al., 2018) or‘fully immersive VR’(Eijlers et al., 2019b), but did not explicitly state their definition of these terms.

It is crucial that these terms are clearly deﬁned and consistently applied to avoid confusion. For example, it can be argued that some of the technologies (e.g., the eMagin 3DVisor) included in Eijlers et al. (2019a)are not fully immersive because their users can still see some of their physical surroundings (seeSlater and Wilbur, 1997). Perhaps more importantly, unclear deﬁnitions of VR and immersion have resulted in an inconsistent inclusion of less advanced technologies that are often referred to as

‘audiovisual glasses’ (AV-glasses), rather than ‘VR’. These

often lack features such as stereoscopy and head tracking, and often have a narrowerﬁeld of view (Wismeijer and Vingerhoets, 2005). However, as review authors do not include‘audiovisual glasses’in their search strategies, many studies using comparable technologies have previously been overlooked. The present review will therefore employ an inclusive deﬁnition of VR and a wider search strategy that also includes AV-glasses. The term

‘VR’will mostly be used in the current study.

OBJECTIVES

Previous reviews have indicated the potential of VR in pediatrics (e.g.,Eijlers et al., 2019a;Iannicelli et al., 2019;Georgescu et al., 2020). However, nearly half of the studies included in the present review were published in 2019 and 2020. As the literature search of the most recent review (Georgescu et al., 2020) was conducted in 2018, an updated review is necessary. Another motivation for the present study is that previous reviews have not quantitively assessed the differences between VR interventions. Considering the potential impact immersion and interactivity may have on treatment effects, such assessments could have important clinical implications.

Previous reviews have reported much heterogeneity in effect sizes (Eijlers et al., 2019b; Georgescu et al., 2020), which may reﬂect VR characteristics, but also differences between medical procedures and patients (e.g., age). The increased number of studies gained from also including AV-glasses will provide greater statistical power to explore these variables as potential sources of the heterogeneity. Identifying any such moderators of treatment effects may help inform the process of designing and

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implementing VR interventions for clinical use. Moreover, the increased number of studies may also provide more accurate estimates of the true effects of using VR during medical procedures.

The present study consists of a systematic literature review and meta-analysis of studies that have used VR to reduce procedural pain and anxiety in pediatrics. It also provides a meta-analytic assessment of the role of VR hardware speciﬁcations (i.e., immersion) and the degree of interaction between the patient and the VR system. The different groups of medical procedures and the age of participants will also be explored as potential moderators of treatment effects.

The research questions were as follows:

• Do VR interventions reduce pain and anxiety in pediatric patients undergoing medical/dental procedures more than standard procedures?

• Does effectiveness of VR interventions vary depending on the type of medical procedure, VR characteristics, and the age of patients?

METHODS

The effects of VR interventions on procedural pain and anxiety in children was evaluated through a systematic literature review and meta-analysis. Reporting will follow the Preferred Reporting Items of Systematic Review and Meta-Analysis (PRISMA) guidelines (Moher et al., 2009).

Protocol and Registration

A study protocol (CRD42020155056) was submitted to the Prospective Register of Systematic Reviews (PROSPERO) in May 2019. Some deviations from the protocol were deemed necessary. Firstly, as the differentiation between ‘VR’ and

‘audiovisual glasses’ was somewhat inconsistent in the

literature, the search strategies were changed to also include

‘audiovisual glasses’ and variants of this term. Due to the

resulting increase in search results, it was necessary to limit the volume of retrieved studies by also adding the terms

‘preparation’, ‘distraction’, ‘pain’, and ‘anxiety’. Secondly, it

was discovered that the reporting of technical specifications of VR systems was poor and inconsistent, particularly in older studies. Selective reporting of technical specifications by authors and VR manufacturers hindered calculations that are required for accurate quantitative comparison in terms of screen resolution andfield of view (see subsections ‘screen resolution’

and ‘ﬁeld of view’). The screen refresh rate was also rarely

disclosed in older studies. Screen resolution, ﬁeld of view and refresh rate were thus omitted from quantitative analyses.

Eligibility Criteria

Study and Publication Characteristics

Studies were considered eligible if a VR intervention was compared experimentally or quasi-experimentally with any non-VR interventions or a no-intervention control group.

Studies with single-case studies and pretest-posttest designs

without control groups were excluded. Unpublished studies were eligible for inclusion. Only publications in English or one of the Scandinavian languages were considered eligible. No time constrains were applied.

Participant Characteristics

Only pediatric samples were eligible for inclusion. Pediatric patients were deﬁned as 0–21 years of age, in accordance with recommendations issued by the American Academy of Pediatrics (Hardin and Hackell, 2017).

Intervention Characteristics

Studies were considered eligible if an intervention involving VR was used to reduce pain and/or anxiety in pediatric patients associated with medical or dental procedures through distraction or procedural preparation. VR was deﬁned as a computer- generated virtual environment presented on a head-mounted device or other VR system that perceptually surrounds the user (i.e., cover all or most of theﬁeld of view). VR presented on conventional screens (with or without 3D-effects) were thus not eligible for inclusion. So-called audiovisual glasses were eligible for inclusion. Augmented reality (AR) technologies render images on a transparent screen that reveals the user’s physical surroundings and were thus excluded.

Outcomes

Questionnaire and observational measures of pain and (state) anxiety were considered eligible. Stress and fear measures were accepted as anxiety measures, as these were thought to have a high degree of conceptual overlap with state anxiety (Öhman, 2008). Studies that used measures of procedural distress were excluded, as this concept includes dimensions of both pain and anxiety (McMurtry et al., 2015). Physiological measures and measures of maladaptive behavior were not considered valid pain or anxiety measures for the same reason.

Comparison Groups

Studies were eligible for inclusion if they compared VR interventions with non-VR interventions or no intervention.

Non-VR interventions may involve non-VR distraction (e.g., television, videogames), non-VR procedural preparation (e.g., verbal or written information about the procedure), standard of care (SOC) procedures or behavior management techniques (e.g., positive reinforcements, tell-show-do technique). The inclusion of both no-intervention, SOC and other non-VR conditions was deemed necessary as Eijlers et al. (2019a) found that standard of care was often poorly deﬁned, and often involved a variety of both pharmacological and non- pharmacological interventions.

Information Sources

The following databases were searched for research articles:

PsycINFO, Web of Science, MEDLINE, EMBASE, SveMed+, Scopus, Google Scholar, Cochrane Central Register of Controlled Trials (CENTRAL), Bieleﬁeld Academic Search Engine (BASE), ClinicalTrials.gov, and International Clinical Trials Registry Platform (ICTRP). The latter three databases

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were included to also identify any ‘gray literature’, such as unpublished studies and theses. Only theﬁrst 150 publications were extracted from Google Scholar, due to the diminishing relevance of hits produced by this search engine. Unpublished studies were collected by contacting researchers identiﬁed in bibliographies, search results or elsewhere. Article reference lists of included studies were also searched manually.

Search

Databases were searched using the following terms and their synonyms: Virtual reality/audiovisual glasses + pediatrics/child + anxiety/pain/preparation/distraction. Search strategies were adapted for each database. The complete search strategy for PsycINFO is presented in Table 1. The last search was conducted October 1, 2020, but manuscripts were received from contacted authors until November 25, 2020.

Study Selection

Upon completion of the literature search and after removal of duplicates, each publication was screened for potential eligibility by thefirst author. Researchers identified in trial registries and conference abstracts were contacted if any corresponding, published research articles were not identified in the search results. The resulting list of studies were considered for eligibility by both authors. Reasons for exclusions were recorded at this point. Any disagreements were resolved through discussion.

Data Collection Process

Data extraction was conducted using an Excel spreadsheet. The spreadsheet was piloted withfive randomly selected studies that were coded independently by both authors. As coding agreement was deemed satisfactory, the remaining data was collected independently by the first author. Numerical study results were coded by the first author and double checked for accuracy by the second author. Any disagreements were resolved through discussion. If sufficient information was not

available in the articles, information was requested from corresponding authors on multiple occasions between May and November 2020. Co-authors were contacted if corresponding authors could not be reached. Efforts were made to locate updated contact information for researchers that did not respond. VR hardware or software speciﬁcations were also sourced from direct communication with manufacturers, technical manuals published online or vendors.

Speciﬁcations sourced directly from articles were preferred, as authors may have reconﬁgured HMD settings.

Data Items

All data items were extracted as speciﬁed in the review protocol. If more than one measure of pain or anxiety were available, retrospectively, self-reported measures were prioritized. Self- reported measures were preferred as pain and anxiety are subjective and private experiences, and because observers’

ability to accurately describe the patient’s distress may be compromised as the VR headsets cover parts of the patient’s face. For pain speciﬁcally, measures of sensory pain were preferred over measures of the affective or cognitive aspects of pain. Final values were preferred over change scores.

The following information was extracted from each primary study: 1) publication and study details (author(s), year published, study design, sample sizes, description of comparison groups); 2) participant characteristics (average age and a measure of dispersion, gender distribution, other health-related characteristics); 3) details regarding the pain and anxiety measures that were used (name of measures, timing of administration, informant); 4) the procedural setting (clinical context in which the procedure took place, the kind of medical procedure, timing of VR intervention); 5) results (keyﬁndings, summary statistics for VR and non-VR groups); 6) VR characteristics (technical speciﬁcations, degree and form of interactivity, and descriptions of media displayed). The VR characteristics (immersion and interactivity) are described in further detail below.

Immersion

The variables describing technical speciﬁcations are primarily based onCummings and Bailenson (2016), who compiled a list of VR features that increase the level of immersion and thus the sense of being present in the virtual environment. The list of VR characteristics included in the present study is not exhaustive, but rather focused on the objective, purely technical properties that were deemed realistic to code. For example, the overall level of detail and realism in virtual environments were not included. In addition to hardware speciﬁcations, information was extracted regarding the number of senses stimulated, the level of user- system interactivity, and the media displayed to participants.

Screen Resolution

The screen resolution refers to the number of pixels the screen displays per frame (Kourtesis et al., 2019). A screen with a high resolution will be perceived to have greaterﬁdelity, or‘crispness’, of images displayed. Resolution is typically reported as horizontal x vertical pixels (e.g., 1,280 × 1800), or pixels per inch (ppi).

TABLE 1 |Search strategy for the PsycINFO database.

1 Exp pediatrics/

2 child*.mp

3 adolescen*.mp

4 boy*.mp

5 girl*.mp

6 1 OR 2 OR 3 OR 4 OR 5

7 Exp virtual reality/

8 audiovisual*.mp

9 7 OR 8

10 Exp. Distraction/

11 Prepar*

12 Exp exposure/

13 Exp pain/

14 Exp analgesia/

15 Exp anxiety

16 anx*.mp

17 10 OR 11 OR 12 OR 13 OR 14 OR 15 OR 16

18 6 AND 9 AND 17

Note.mp.ﬁeld code for title, abstract, heading word, table of contents, key concepts, original title, tests and measures, mesh.

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However, as pointed out byHugues (2019), the pixel per degree (ppd) format more truly reflects thefidelity of the display, as it is independent of the field of view. Calculating the ppd requires knowledge of the horizontal field of view, which is rarely disclosed. The screen resolutions were therefore not compared quantitively.

Field of View

The field of view (FoV) refers to the degrees of the VR user’s visual field that is occupied by the virtual environment (Cummings and Bailenson, 2016). FoV may be reported as diagonal, horizontal or vertical. Manufacturers oftentimes reveal only one measure (diagonal) of the FoV, whereas others withhold this information completely. The FoV may also be artificially increased by reducing the stereo overlap, i.e., the area of the screen in which the user can perceive depth (Hugues, 2019). It was thus decided that the field of view of devices could not be quantitively, fairly compared and this variable was omitted from quantitative synthesis.

Screen Refresh Rate

The screen refresh rate refers to the rate at which the screens update the images displayed on the screen, based on input generated by the computer (Kourtesis et al., 2019). A low screen refresh rate would be perceived as a lack of ﬂuency in images, or a lag between the user’s actions and visual input. The screen refresh rate is either reported in cycles per second (Hz) or frames per second (FPS). As this information was frequently missing, particularly in older studies, the screen refresh rate was not used to compare VR interventions.

Stereoscopy/Three-Dimensional Graphics

Stereoscopy is achieved by presenting separate images to each eye with slight differences in perspective that reﬂects the interpupillary distance. It provides an illusion of depth in the virtual environment and may increase immersion (Yang et al., 2012).

Head Tracking

Some VR systems track user movements and use this information to adjust images (and sometimes sound) accordingly. All parts of the body can be tracked, but tracking of head movements is the most common. According toSlater (2009), tracking strengthens the illusion of being present in the virtual environment as the participant can perceive through natural sensorimotor contingencies (O’Regan and Noë, 2001). For example, a participant may tilt his or her head to inspect a virtual object from several angles, which is not possible on conventional screens.

Visual Occlusion

This variable refers to whether the VR system fully covered the participant’s physical surroundings. HMDs that are not fully occlusive may have a gap between the device and the participant’s face that lets light through and allows the participant to see parts of the procedural setting. Minimizing input from the physical reality may strengthen the illusion of

being present in the virtual environment (Slater and Wilbur, 1997).

Non-Visual Sensory Stimulation

This variable described whether the VR intervention involved any non-visual, sensory stimulation. This would typically be in the form of auditive stimuli (e.g., music or sound effects from games), but also tactile stimuli (e.g., force feedback or vibration from controllers). Researchers may choose not to include audio to avoid disruption in communication between patients and personnel delivering the medical procedures. However, it is commonly assumed that multisensory stimuli provide greater immersion and sense of presence (Cummings and Bailenson, 2016).

Interactivity

This variable was used to declare whether the VR system offered any user-system interaction beyond control of theﬁeld of view (i.e., tracking of head movements). Interactivity may for example include navigation in the virtual environment or manipulation of virtual objects.

Risk of Bias Assessment in Individual Studies

Assessment of study risk of bias was conducted in accordance with the Cochrane Handbook of Systematic Reviews (Higgins et al., 2020). The effect of interest was the Intention-To-Treat (ITT) effect, i.e., the effect of allocation to intervention. Risk of bias was assessed at outcome level independently by the first author. The ROB 2.0 (Sterne et al., 2019) and ROBINS-I (Sterne et al., 2016) tools were used for RCTs and non-randomized studies, respectively. The RCT characteristics assessed were 1) bias arising from the randomization process, 2) bias due to deviations from intended interventions, 3) bias due to missing outcome data, 4) bias in measurement of the outcome, and 5) bias in selection of the reported result. Additional considerations for cross-over trials were applied (Sterne et al., 2019). However, they were evaluated with the parallel design tool if only data from the first study period was analyzed. Non-randomized studies were evaluated in terms of the following domains: 1) confounding, 2) selection bias, 3) bias in classification of interventions, 4) bias due to deviations from intended interventions, 5) bias due to missing data, 6) bias in measurement of outcomes, and 7) bias in selection of the reported result. The risk of bias judgements for each domain are illustrated in separate figures for randomized and non-randomized studies. Additional bar plots illustrate the overall judgment for each domain across studies, with each study’s contribution weighted by their standard error. The figures were constructed using the robvis web application (McGuinness and Higgins, 2020). A separate, additional analysis excluding studies deemed to have a high risk of bias in two or more domains was conducted.

Summary Measures

The differences in mean pain and anxiety scores for the VR and control groups were calculated as Hedges’ g (Hedges, 1981).

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While similar tod,the Hedges’gincludes a correction term that yields a less biased estimate, particularly when sample sizes are small (Borenstein et al., 2009). If a study had multiple VR or non- VR arms, summary statistics were combined by calculating their weighted mean and standard deviations, based on their number of participants. Means and standard deviations were ideally extracted directly from articles or obtained from study authors. If necessary, they were estimated. Sample means were estimated from the median by the method ofShi et al. (2020).

Estimation of variance based on the median, interquartile range and sample sizes were based on the method ofWan et al. (2014).

For studies that also reported the minimum and maximum values, the formula proposed byLuo et al. (2018)was used for additional precision. These estimations were performed using an online calculator byShi et al. (2020). The Campbell Collaboration effect size calculator (Wilson, n.d.) was further used to estimate effect sizes fromt-statistics.

Cross-over trials were only included for quantitative synthesis if data from theﬁrst study period only was available.

Several studies reported multiple measures of pain and anxiety. As specified in the review protocol, only one measure for each outcome was used for quantitative synthesis. The selection was based on the following pre-specified criteria: 1) Self-reported measures were preferred over observational measures; 2) measures of sensory pain were preferred over measures of the cognitive or affective aspects of pain. If two or more measures fit the abovementioned criteria, the most frequently used measure was selected.

Synthesis of Results

The methodology was guided byBorenstein et al. (2009)and the Cochrane handbook of systematic reviews (Higgins et al., 2020).

All statistical analyses (except selection models) were conducted using Stata 16 (StataCorp, 2019). Standardized mean differences in pain and anxiety were combined using a random-effects model. The random-effects model assumes that the study effect sizes are drawn from different populations of study effect sizes, i.e., that observed variance consists of both sampling error and differences in true effect sizes (Borenstein et al., 2009). This model was selected as the studies were expected to be diverse in terms of study designs, participant characteristics, medical procedures, and VR characteristics, to name a few. The restricted maximum likelihood estimator of between-studies variance (τ²) was selected based on recommendations by Veroniki et al. (2016). The results of the two meta-analyses are presented in separate forest plots.

The magnitude of effect sizes will be compared with those of comparable studies, as compiled byLipsey and Wilson (1993).

The standardized mean effect will also be expressed as absolute mean differences on the Wong-Baker Faces scale and the Child Fear Scale. These scales were selected as they were the most frequently used one-item scales among the outcomes included in the meta-analysis. The absolute mean difference will be calculated by multiplying the standardized mean difference with the combined standard deviations from every study in which these measures were used in the meta-analysis (Schünemann et al., 2020).

Heterogeneity among all included studies was assessed by consulting the Cochran’sQtest. A signiﬁcant result indicates that the observed variation in effect sizes reﬂects true heterogeneity (Borenstein et al., 2009). TheI²statistic was then used to quantify the magnitude of heterogeneity. It describes the percentage of total variation that is due to heterogeneity (Higgins et al., 2003), with higher values indicating greater heterogeneity.

Risk of Bias Across Studies

Publication bias compromises the validity of the results of meta- analyses and systematic reviews. The term is typically used to refer to the selective publication of studies with a particular outcome, most often “positive” or statistically signiﬁcant results (Ferguson and Brannick, 2012; Augusteijn et al., 2019;

Vevea et al., 2019). This tendency leads to an over-estimation of the summary effect sizes, in particular when the population of studies being sampled from is characterized by low statistical power (Ioannidis, 2008;Button et al., 2013).

We followed recommendations to assess publication bias using a number of different methods in a sensitivity analysis approach, since no single method alone provides reliable results (Carter et al., 2019; Vevea et al., 2019). Publication bias was assessed visually with a funnel plot (Light and Pillemer, 1984) in which study effect sizes (horizontal axis) were plotted against their inverse standard error (vertical axis). Areas representing three intervals of p-values (contours) were added to facilitate interpretation (Peters et al., 2008). As the standard error is directly related to the number of participants, plot asymmetry may be indicative of small-study effects (Sterne et al., 2005).

Visual inspection was supported by statistically testing for asymmetry using Egger’s test, which involves regression analysis of the relationship between effect sizes and their standard error (Egger et al., 1997; Sterne and Egger, 2005). If the regression intercept differs from zero, this may indicate publication bias.

The trim-and-fill algorithm (Duval and Tweedie, 2000) was used to estimate an effect size adjusted for publication bias. This procedure is conducted in two steps. During thefirst step, studies that cause funnel plot asymmetry are removed from the mean effect size estimate until symmetry is achieved (iteration step) (Borenstein et al., 2009). An adjusted mean effect size is then estimated. The removed studies arefinally re-applied, along with the studies that are assumed to be missing from either side of the funnel plot (pooling step). Thisfinal step estimates the variance of the new mean effect size. The trim-and-fill method is widely used, but its performance may vary depending on the presence of substantial heterogeneity or outlying studies, as well as which combination of models, methods, and estimators that is used. Researchers are thus encouraged to use various versions of the trim-and-fill method (Shi and Lin, 2019). In the present study, fixed- and random-effects (restricted maximum likelihood method) models with the linear (L0) and run (R0) estimators were used.

The methods above are all based on assessing funnel plot asymmetry. While publication bias will lead to asymmetry, asymmetry can also occur for a number of other reasons (Vevea et al., 2019). Furthermore, tests for asymmetry may lead to misleading results under conditions of high between-study

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heterogeneity (van Aert et al., 2019). An alternative is to model study selection more directly (Hedges and Vevea, 2005). We adopted the selection model approach proposed by Vevea and Woods (2005), which essentially estimates the robustness of meta- analytic effect size estimates to hypothetical patterns of selection bias. Speciﬁcally, we modeled three different selection probabilities (0.2, 0.5, and 0.8) for conventionally non-signiﬁcant studies, representing severe, moderate and mild publication bias, respectively. Selection models were run using the weightr package for R (Coburn and Vevea, 2019).

Additional Analyses

Subgroup and Meta-Regression Analyses

Moderator analyses were conducted to explore potential sources of heterogeneity in effect sizes. The differences between subsets of the studies were initially explored with subgroup analyses.

Categorical and continuous variables were then used as predictors in a random-effects meta-regression analysis. It is generally recommended that there are approximately ten studies per predictor (Borenstein et al., 2009). As the present study was focused on the differences between VR interventions, these variables were prioritized in the meta-regression analysis rather than the kind of medical procedure.

As previously discussed, the screen refresh rate, resolution and field of view were omitted from quantitative analysis due to insufficient information. After coding the remaining immersion variables, it was discovered that only one study included any non-visual stimuli. This variable was thus also omitted from the composite immersion variable. As information regarding the four remaining immersion variables was lacking for several studies, it was decided to code VR interventions as either highly immersive (included auditive stimuli, head tracking, stereoscopy/three-dimensional images, and full visual occlusion) or less immersive/insufficient information. The VR interventions were also coded as either interactive or passive (i.e., no interactivity beyond head tracking).

Medical procedures were categorized as either‘dental’,‘needle- related procedures’,‘pre-operative’, or‘wound care’. The mean study-level age was included as a continuous variable. All potential moderators were pre-speciﬁed in the review protocol.

Sensitivity Analyses

Sensitivity analyses were conducted to ensure that the summary effect estimates were robust to the removal of the following studies: 1) under-powered studies, 2) non-randomized studies, and 3) studies deemed to have a high risk of bias in two or more domains. Assuming a one-tailed alpha of 0.05 and an 80% power to detect an effect size of 0.50, studies were considered under- powered if they had less than 50 participants in each group (Cohen, 1988).

RESULTS

65 primary studies derived from 64 articles published between 2000–2021 were included in qualitative synthesis. 13 studies were not included in the meta-analyses due to missing numerical

results (Gershon et al., 2004; Khan et al., 2019), only change from baseline scores being reported (Kipping et al., 2012), or insufﬁcient data to include cross-over trials (Sullivan et al., 2000;

Das et al., 2005;Chan et al., 2007;El-Sharkawi et al., 2012;Attar and Baghdadi, 2015; Atzori et al., 2018a; Atzori et al., 2018b;

Garrocho-Rangel et al., 2018;Hoffman et al., 2019;Koticha et al., 2019). Two data sets were obtained from contact with authors to calculate the effect size for theﬁrst study period only (Schmitt et al., 2011) and summary statistics (Jeffs et al., 2014). Two unpublished studies were acquired by contacting authors identiﬁed in the trial registries (Gerceker et al., 2021;

Osmanlliu et al., 2021). Another two published manuscripts were received from contacted authors after the ﬁnal database search was conducted (Buldur and Candan, 2020;Litwin et al., 2020). The process of study selection is illustrated inFigure 1.

Qualitative results and study characteristics are presented in Table 2. VR characteristics are listed in Table 3. A narrative synthesis of study and VR characteristics is presented in the following paragraphs.

Study Characteristics

Most of the studies (k61) were RCTs, of which 43 employed a parallel-groups design and 18 studies employed a cross-over design. Four non-randomized studies were included.

Participant Characteristics

The total number of participants included in the qualitative review was 4,654, with sample sizes ranging from 5 to 220, and averaging at 72 participants. Included participants were between 6 months and 21 years of age, and the mean study- level age was 9.23 years 4,162 participants were included in the quantitative analysis. Sample sized ranged from 20 to 220, with an average of 80 participants. The mean study-level age of these children were 9.13 years of age.

Measures

Self-reported measures of pain were available in all but two studies (Wolitzky et al., 2005; Khadra et al., 2020), whereas observational measures had to be used for 11 of the anxiety studies. The Wong-Baker Faces Scale (Wong-Baker FACES Foundation, 2018) and the (revised) Faces Pain Scale (Hicks et al., 2001) were the most widely used pain measures, followed by visual analogue scales ([VAS],Bailey et al., 2012). VAS scales were also frequently used to measure anxiety. The most used observational measure of anxiety was the modiﬁed Yale Preoperative Anxiety Scale (Kain et al., 1997).

Settings and Medical Procedures

Studies were mostly conducted in pediatric hospitals or dental clinics. Most of the procedures were classiﬁed as needle-related procedures (k25), followed by dental (k24), pre-operative (k8), and wound care (k8).

Intervention Characteristics

Most of the distraction studies (k61) used VR as a distraction during the medical procedures. OnlyAl-Nerabieah et al. (2020) used VR as a distractionbeforethe procedure (i.e., in the waiting

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room before dental procedures). In one cross-over trial, the effect of receiving VR distraction during the ﬁrst treatment on pre- operative anxiety before the second treatment could be extracted (Fakhruddin et al., 2015).

Four studies (Eijlers et al., 2019a;Ryu et al., 2017;Ryu et al., 2018;Ryu et al., 2019) were categorized as preparation studies.

These VR interventions involved virtual tours of the pre- operative settings, in which children were exposed to the procedural environment and medical personnel, as well as information about the procedures. Ryu and colleagues incorporated popular cartoon ﬁgures that explained and modeled the procedures. Participants in Eijlers, et al. (2019b) and Ryu et al. (2018) were also able to interact with virtual medical devices and receive further information about them.

Virtual Reality Characteristics

Head-mounted devices (HMDs) were used in all but three studies (k62). InKhadra et al. (2020), patients were placed in front of a

wide, curved screen that images were displayed on with a projector. This study was included as the screen covered the majority of the patient’s ﬁeld of view and resembled a surrounding, dome-based VR system. Jeffs et al. (2014) and Hoffman et al. (2019) used HMDs that were mounted on either a tripod or a robotic arm to facilitate participation by patients with burn injuries in the head and neck region, or to facilitate use during hydrotherapy. In 28 studies, so-called smartphone-based systems were used in which a smartphone or other device is inserted into the HMD to serve as the screen and tracking device (Fuchs, 2019). The most common combination was the Samsung Gear headset coupled with various Samsung smartphones.

As previously mentioned, information regarding at least some technical speciﬁcations were lacking for many studies, particularly in older studies and in studies that used less advanced VR systems. However, it was clear that the quality of the VR equipment varied considerably between studies. 37 of

FIGURE 1 |PRISMAﬂowchart showing the study selection process.

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TABLE 2 |Study characteristics and results.

Kind of medical procedure

Author(s) (year) Study design

Participants Measures Procedural

setting

Purpose of VR

Comparison group(s)

Keyﬁndings N Age Range

M(SD)

Pain Anxiety

Dental Al-Halabi et al. (2018) RCT parallel 101 6–10 W-B N/A Pediatric dentistry Distraction during inferior alveolar nerve block

Control (basic behavior guidance techniques); tablet distraction

No differences in pain scores between the groups

7.40 Faces

(-) Al-Khotani et al.

(2016)

RCT parallel 56 7–9 N/A FIS Pediatric dentistry Distraction during restorative treatment

Control (no distraction) No difference in anxiety scores according to FIS.

Lower anxiety scores in AV group according to MVARS

8.20 (0.80)

Al-Nerabieah et al.

(2020)

RCT parallel 64 6–10 W-B faces mYPAS-SF Pediatric dentistry Distraction in the waiting room before dental procedures

No distraction Lower pain and anxiety scores in the VR group 7.50

(1.30) Aminabadi et al.

(2013)

RCT cross- over

120 4–6 W-B faces MCDAS(f) Pediatric dentistry Distraction during restorative treatment

No VR distraction Lower pain and anxiety scores during VR 5.42

(0.73) Asvanund et al. (2015) RCT cross-

over

49 5–8 FPS-R N/A Pediatric dental

clinic

Distraction during restorative treatment

Behavior management techniques

Lower pain scores during VR 7.00

(0.87) Attar and Baghdadi

(2015)

RCT cross- over

39 4–8^a W-B faces N/A Pediatric dental

clinic

Distraction during administration of local anesthesia

iPad distraction Higher pain scores during VR 6.27 (1.24)

Atzori et al. (2018a) RCT cross- over

5 7–17 GRS N/A Private dental

clinic

Distraction during dentalﬁllings or tooth extraction

No VR Lower sensory and

affective pain scores during VR 13.20 (2.39)

Bagattoni et al. (2018) RCT cross- over

48 5–10 FPS-R N/A Special needs/

pediatric dentistry

Conventional behavior management (protective eyeglasses)

No difference between the groups on study day I, but lower pain scores for audiovisual group on study day II

7.30 (1.50)

Buldur and Candan (2020)

RCT cross- over

76 7–11 W-B faces FIS Pediatric dentistry Distraction during routine dental treatment

Control/placebo (protective eyeglasses)

No difference between the groups on any self- reported measures 9.02

(1.39) Chaudhary et al.

(2020)

RCT parallel 60 - W-B faces N/A Pediatric dentistry Distraction during administration of inferior alveolar nerve block (IANB)

Control (behavior management techniques;

no distraction)

Lower pain scores in VR group

El-Sharkawi et al.

(2012)

RCT cross- over

48 5–7 FPS N/A Pediatric dentistry Distraction during

administration of inferior alveolar nerve block (IANB)

Control (tell-show-do technique; topical anesthesia)

Lower pain scores during VR/AV-distraction (-)

Fakhruddin et al.

(2015)

RCT cross- over

60 4–7 W-B faces MCDAS(f) Dental hospital Distraction during pulp therapy

Non-VR distraction (projector display)

Lower pain scores in AV group. AV-glasses during visit I reduced pre- operative anxiety before visit II

5.24 (1.20)

(Continued on following page)

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TABLE 2 |(Continued) Study characteristics and results.

Kind of medical procedure

Author(s) (year) Study design

Participants Measures Procedural

setting

Purpose of VR

Comparison group(s)

Keyﬁndings N Age Range

M(SD)

Pain Anxiety

Garrocho-Rangel et al. (2018)

RCT cross- over

36 5–8 FLACC N/A Pediatric dentistry Distraction during

dental treatment

Control (behavior management techniques)

No difference in pain scores between the AV and control groups 6.20

(1.30)

Hoge et al. (2012) RCT parallel 128 4–16 FPS-R N/A Dental clinic Distraction during

restorative treatment

Sunglasses No difference in pain scores between the AV and control groups 9.31 (2.79)

Isong et al. (2014) RCT parallel 40 7–17^a N/A VARS Pediatric dental

clinic

Distraction during preventative treatment

SOC (no intervention) Lower anxiety scores in AV group

9.95 (2.80)

Khan et al. (2019) RCT parallel 100 4–10 N/A FIS Pediatric/

preventive dentistry

Distraction during restorative dental treatment

SOC No difference between VR

and non-VR group 6.36

(-) Koticha et al. (2019) RCT cross-

over

60 7–17 N/A VPT Pediatric/

preventive dentistry

Distraction during tooth extraction

No VR No difference in self-

reported anxiety between groups

13.20 (2.39) Mitrakul et al. (2015) RCT cross-

over

42 7–17^a FPS-R N/A Pediatric dentistry Distraction during restorative treatment

Behavior management techniques

AV glasses reduced pain during dental treatment 10.92 (2.64)

Niharika et al. (2018) RCT cross- over

40 4–8 W-B faces MCDAS(f) Pediatric dentistry Distraction during pulp therapy

No VR VR reduced pain and

anxiety scores 7.23 (0.31)

Nunna et al. (2019) RCT parallel 70 7–11 VAS VCARS^a Pediatric dentistry Distraction during administration of local anesthesia

Counter-stimulation No difference in pain scores, but higher anxiety scores in VR group 8.86 (1.41)

Nuvvula et al. (2014) RCT parallel 90 7–10 N/A MCDAS(f) Pediatric/

preventative dentistry

Distraction during administration of local anesthesia

Control (standard behavior techniques); music (music distraction + standard behavior techniques)

AV-glasses reduced anxiety

8.40 (-)

Shah and Bhatia.

(2018)

RCT parallel 50 4–7 N/A FIS Pediatric/

preventative dentistry

Tell-play-do technique No difference in anxiety scores between groups (-)

Shetty et al. (2019) RCT parallel 120 5–8 W-B faces MCDAS(f)-r Pediatric dentistry Distraction during pulp therapy

Conventional behavior management techniques

Virtual reality reduced pain and anxiety

6.76 (1.03) Sullivan et al. (2000) Within-

groups design N-R

30 5–7 N/A KRS Dental clinic Distraction during

administration of local anesthesia

No VR No difference between

groups (-)

Needle- related proce-dures

Atzori et al. (2018b) RCT cross- over

15 7–17 VAS N/A Pediatric onco-

hematological setting

Distraction during venipuncture

SOC (no VR; non-medical conversation with nurse)

Lower affective, cognitive, and sensory pain scores during VR

10.92 (2.64) Aydin and

Ozyazicioglu (2019)

RCT parallel 120 9–12 VAS; W-B

faces

N/A Pediatric hospital Distraction during phlebotomy

No intervention Lower pain scores in VR group

10.40 (1.13)

Caruso et al. (2019) RCT parallel 220 7–18 FPS-R CFS Pediatric hospital Distraction during vascular access

Various standard coping methods

No signiﬁcant differences in pain or anxiety scores 13.6 (3.10)

Chan et al. (2019) (study I, emergency department)

RCT parallel 123 4–11 FPS-R VAT Emergency

department in pediatric hospital

Distraction during venipuncture or cannulation

SOC (various distraction techniques)

VR reduced pain 8.06 (2.42)

Chan et al. (2019) (study II, pathology)

RCT parallel 129 4–11 FPS-R VAT Pathology

(outpatient) in pediatric hospital

Distraction during venipuncture

SOC (various distraction techniques)

Lower change from baseline scores in VR group

7.8 (2.33)

(Continued on following page)

FrontiersinVirtualReality|www.frontiersin.orgJuly2021|Volume2|Article69938311 NordgårdandLågMeta-AnalysisofVRinPediatrics